August 4, 2021

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A Privacy-Preserving and Trustable Multi-agent Learning Framework. (arXiv:2106.01242v1 [cs.LG])

Distributed multi-agent learning enables agents to cooperatively train a
model without requiring to share their datasets. While this setting ensures
some level of privacy, it has been shown that, even when data is not directly
shared, the training process is vulnerable to privacy attacks including data
reconstruction and model inversion attacks. Additionally, malicious agents that
train on inverted labels or random data, may arbitrarily weaken the accuracy of
the global model. This paper addresses these challenges and presents
Privacy-preserving and trustable Distributed Learning (PT-DL), a fully
decentralized framework that relies on Differential Privacy to guarantee strong
privacy protections of the agents’ data, and Ethereum smart contracts to ensure
trustability. The paper shows that PT-DL is resilient up to a 50% collusion
attack, with high probability, in a malicious trust model and the experimental
evaluation illustrates the benefits of the proposed model as a
privacy-preserving and trustable distributed multi-agent learning system on
several classification tasks.